use_t (bool) – If true, then the t distribution is used for inference.
If false, then the normal distribution is used.
If use_t is None, then an appropriate default is used, which is
true if the cov_type is nonrobust, and false in all other
cases.

results – This method creates a new results instance with the
requested robust covariance as the default covariance of
the parameters. Inferential statistics like p-values and
hypothesis tests will be based on this covariance matrix.

Return type:

results instance

Notes

The following covariance types and required or optional arguments are
currently available:

‘fixed scale’ and optional keyword argument ‘scale’ which uses

a predefined scale estimate with default equal to one.

‘HC0’, ‘HC1’, ‘HC2’, ‘HC3’ and no keyword arguments:

heteroscedasticity robust covariance

‘HAC’ and keywords

maxlag integer (required) : number of lags to use

kernel callable or str (optional) :kernel

currently available kernels are [‘bartlett’, ‘uniform’],
default is Bartlett

use_correction bool (optional) :If true, use small sample

correction

‘cluster’ and required keyword groups, integer group indicator

groups array_like, integer (required) :

index of clusters or groups

use_correction bool (optional) :

If True the sandwich covariance is calculated with a small
sample correction.
If False the sandwich covariance is calculated without
small sample correction.

df_correction bool (optional)

If True (default), then the degrees of freedom for the
inferential statistics and hypothesis tests, such as
pvalues, f_pvalue, conf_int, and t_test and f_test, are
based on the number of groups minus one instead of the
total number of observations minus the number of explanatory
variables. df_resid of the results instance is adjusted.
If False, then df_resid of the results instance is not
adjusted.

‘hac-groupsum’ Driscoll and Kraay, heteroscedasticity and

autocorrelation robust standard errors in panel data
keywords

time array_like (required) : index of time periods

maxlag integer (required) : number of lags to use

kernel callable or str (optional) :kernel

currently available kernels are [‘bartlett’, ‘uniform’],
default is Bartlett

use_correction False or string in [‘hac’, ‘cluster’] (optional) :

If False the the sandwich covariance is calulated without
small sample correction.
If use_correction = ‘cluster’ (default), then the same
small sample correction as in the case of ‘covtype=’cluster’’
is used.

errors in panel data.
The data needs to be sorted in this case, the time series
for each panel unit or cluster need to be stacked. The
membership to a timeseries of an individual or group can
be either specified by group indicators or by increasing
time periods.

keywords

either groups or time : array_like (required)
groups : indicator for groups
time : index of time periods

maxlag integer (required) : number of lags to use

kernel callable or str (optional) :kernel

currently available kernels are [‘bartlett’, ‘uniform’],
default is Bartlett

use_correction False or string in [‘hac’, ‘cluster’] (optional) :

If False the sandwich covariance is calculated without
small sample correction.